{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练，验证，测试集\n",
    "## 神经网络的超参数\n",
    "1. 神经网络分多少层\n",
    "2. 每层含有多少个隐藏单元\n",
    "3. 学习速率是多少\n",
    "4. 各层采用哪些激活函数\n",
    "<img src=\"images/072653e2e9402d2857bfcb7b9f783a5c.png\" >\n",
    "\n",
    "## 不断迭代\n",
    "即使是经验丰富的深度学习行家也不太可能一开始就预设出最匹配的超级参数，所以说，应用深度学习是一个典型的迭代过程，需要多次循环往复，才能为应用程序找到一个称心的神经网络，因此循环该过程的效率是决定项目进展速度的一个关键因素，而创建高质量的训练数据集，验证集和测试集也有助于提高循环效率。\n",
    "<img height=\"70%\" width=\"70%\" src=\"images/163d77ba74a55f627d201cd2d9ae8f07.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 偏差,方差(Bias/Variance)\n",
    "## 欠拟合,过拟合\n",
    "<img src=\"images/05ac08b96177b5d0aaae7b7bfea64f3a.png\" >\n",
    "\n",
    "## 训练集误差和验证集误差\n",
    "<img src=\"images/c61d149beecddb96f0f93944320cf639.png\" >\n",
    "\n",
    "## 总结\n",
    "1. 偏差-欠拟合-训练集表现\n",
    "2. 方差-过拟合-验证集和训练集的表现\n",
    "<img height=\"70%\" width=\"70%\" src=\"images/1cde763e4f25e7e576312e5ce22fc623.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 改进偏差和方差表现的通用方法\n",
    "<img width=\"70%\" src=\"images/161d983f9cdcc586a5b79b3161721d6c.png\" >\n",
    "\n",
    "## 欠拟合\n",
    "<img width=\"60%\" src=\"images/164248d8d8346f322c913b9a4244e71c.png\" >\n",
    "\n",
    "## 过拟合\n",
    "<img width=\"60%\" src=\"images/79253524fc02afade55a7b95704e1a27.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则化（Regularization）\n",
    "<img width=\"60%\" src=\"images/e96ab8c5d7ddbb08eb73e550a6a2cc1e.png\" >\n",
    "\n",
    "## $L2$正则化\n",
    "### 逻辑回归\n",
    "<img src=\"images/2e88bd6f30a8ff8014d6d7dbe6d0488a.png\" >\n",
    "\n",
    "### 神经网络\n",
    "<img src=\"images/5663bd9360df02b7e5a04c01d4e1bbc7.png\" >\n",
    "\n",
    "### $L2$正则化后梯度下降公式\n",
    "$W:=W-\\alpha(dW+\\frac{\\lambda}{m}W)=(1-\\frac{\\alpha\\lambda}{m})W-\\alpha dW$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 为什么正则化有利于预防过拟合呢？\n",
    "<img src=\"images/05ac08b96177b5d0aaae7b7bfea64f3a.png\" >\n",
    "\n",
    "## $L2$正则化后梯度下降公式\n",
    "$W:=W-\\alpha(dW+\\frac{\\lambda}{m}W)=(1-\\frac{\\alpha\\lambda}{m})W-\\alpha dW$\n",
    "<img src=\"images/2aafa244c3f184cc271b26d1d95d70c9.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dropout(随机失活)正则化\n",
    "<table>\n",
    "<tr>\n",
    "    <td><img src=\"images/97e37bf0d2893f890561cda932ba8c42.png\" ></td>\n",
    "    <td><img src=\"images/e45f9a948989b365650ddf16f62b097e.png\" ></td>\n",
    "    <td><img src=\"images/9fa7196adeeaf88eb386fda2e9fa9909.png\" ></td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 理解dropout(随机失活)\n",
    "1. 不过度依赖某个单元;\n",
    "2. 随机失活-输出为0-$L2$正则化效果:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 其他正则化方法\n",
    "## 数据扩增\n",
    "<img src=\"images/b4aab3fe785809ed4f32c9d2d5ec38fa.png\" >\n",
    "\n",
    "## **early stopping**\n",
    "<img src=\"images/9d0db64a9c9b050466a039c935f36f93.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 归一化输入\n",
    "## 归一化:\n",
    "1. 零均值\n",
    "1. 方差归一化\n",
    "<img src=\"images/5e49434607f22caf087f7730177931bf.png\" >\n",
    "\n",
    "## 为什么?\n",
    "<img src=\"images/4d0c183882a140ecd205f1618243d7f8.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 梯度消失/梯度爆炸\n",
    "<img src=\"images/fc03196f0b6d1c9f56fa39d0d462cfa4.png\" >\n",
    "\n",
    "假设中间隐含层的每个权重矩阵$W^{[l]} = \\begin{bmatrix} 1.5 & 0 \\\\0 & 1.5 \\\\\\end{bmatrix}$，$W^{[L]} = \\begin{bmatrix} 1 & 1\\end{bmatrix}$<br>\n",
    "$\\Rightarrow\\hat y= W^{[L]}\\begin{bmatrix} 1.5 & 0 \\\\ 0 & 1.5 \\\\\\end{bmatrix}^{(L -1)}x$<br>\n",
    "如果对于一个深度神经网络来说$L$值较大，那么$\\hat{y}$的值也会非常大，实际上它呈指数级增长的，它增长的比率是${1.5}^{L}$，因此对于一个深度神经网络，$y$的值将爆炸式增长。\n",
    "\n",
    "相反的，如果中间隐含层的每个权重矩阵$W^{[l]} = \\begin{bmatrix} 0.5& 0 \\\\ 0 & 0.5 \\\\ \\end{bmatrix}$，那么$\\hat y= W^{[L]}\\begin{bmatrix} 0.5 & 0 \\\\ 0 & 0.5 \\\\\\end{bmatrix}^{(L - 1)}x$，激活函数的值将以指数级下降，它是与网络层数数量$L$相关的函数，在深度网络中，激活函数以指数级递减。\n",
    "\n",
    "同样,与层数$L$相关的导数或梯度函数，也是呈指数级增长或呈指数递减。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 神经网络的权重初始化\n",
    "<img src=\"images/db9472c81a2cf6bb704dc398ea1cf017.png\" >\n",
    "\n",
    "$z = w_{1}x_{1} + w_{2}x_{2} + \\ldots +w_{n}x_{n}$，$b=0$，暂时忽略$b$，为了预防$z$值过大或过小，你可以看到$n$越大，你希望$w_{i}$越小，因为$z$是$w_{i}x_{i}$的和，如果你把很多此类项相加，希望每项值更小，最合理的方法就是设置$w_{i}=\\frac{1}{n}$，$n$表示神经元的输入特征数量，实际上，你要做的就是设置某层权重矩阵$w^{[l]} = np.random.randn( \\text{shape})*\\text{np.}\\text{sqrt}(\\frac{1}{n^{[l-1]}})$，$n^{[l - 1]}$就是我喂给第$l$层神经单元的数量（即第$l-1$层神经元数量）。\n",
    "\n",
    "对于$tanh$等激活函数一般:高斯分布乘以$\\sqrt{\\frac{1}{n^{[l-1]}}}$,最新的推荐是$\\sqrt{\\frac{2}{n^{[l-1]} + n^{\\left[l\\right]}}}$\n",
    "\n",
    "对于**Relu**激活函数,使用的是$\\sqrt{\\frac{2}{n^{[l-1]}}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 神经网络权重初始化-练习\n",
    "训练神经网络需要初始化网络的权重参数.一个好的初始化方法有利于网络的训练学习.接下来我们,练习之前学到的不同的初始化方法,并且观察实验结果.\n",
    "\n",
    "一个优秀的权重初始化方法:\n",
    "1. 能够加速梯度下降的收敛速度;\n",
    "2. 降低训练偏差;\n",
    "\n",
    "## 环境准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0)\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] #指定默认字体,用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False #解决保存图像是负号'-'显示为方块的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_dataset():\n",
    "    np.random.seed(1)\n",
    "    train_X, train_Y = sklearn.datasets.make_circles(n_samples=300, noise=.05)\n",
    "    np.random.seed(2)\n",
    "    test_X, test_Y = sklearn.datasets.make_circles(n_samples=100, noise=.05)\n",
    "    plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y, s=40, cmap=plt.cm.Spectral);\n",
    "    train_X = train_X.T\n",
    "    train_Y = train_Y.reshape((1, train_Y.shape[0]))\n",
    "    test_X = test_X.T\n",
    "    test_Y = test_Y.reshape((1, test_Y.shape[0]))\n",
    "    return train_X, train_Y, test_X, test_Y\n",
    "\n",
    "train_X, train_Y, test_X, test_Y = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经网络模型\n",
    "我们以三层神经网络举例来实验以下不同的参数初始化方法:\n",
    "1. 零参数;\n",
    "2. 随机参数;\n",
    "3. \"He\"参数;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    s = 1/(1+np.exp(-x))\n",
    "    return s\n",
    "\n",
    "def relu(x):\n",
    "    s = np.maximum(0,x)\n",
    "    return s\n",
    "def forward_propagation(X, parameters):        \n",
    "    # retrieve parameters\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]    \n",
    "    # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    z1 = np.dot(W1, X) + b1\n",
    "    a1 = relu(z1)\n",
    "    z2 = np.dot(W2, a1) + b2\n",
    "    a2 = relu(z2)\n",
    "    z3 = np.dot(W3, a2) + b3\n",
    "    a3 = sigmoid(z3)    \n",
    "    cache = (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3)    \n",
    "    return a3, cache\n",
    "def compute_loss(a3, Y):   \n",
    "    m = Y.shape[1]\n",
    "    logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
    "    loss = 1./m * np.nansum(logprobs)\n",
    "    \n",
    "    return loss\n",
    "def backward_propagation(X, Y, cache):\n",
    "    m = X.shape[1]\n",
    "    (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3) = cache\n",
    "    \n",
    "    dz3 = 1./m * (a3 - Y)\n",
    "    dW3 = np.dot(dz3, a2.T)\n",
    "    db3 = np.sum(dz3, axis=1, keepdims = True)\n",
    "    \n",
    "    da2 = np.dot(W3.T, dz3)\n",
    "    dz2 = np.multiply(da2, np.int64(a2 > 0))\n",
    "    dW2 = np.dot(dz2, a1.T)\n",
    "    db2 = np.sum(dz2, axis=1, keepdims = True)\n",
    "    \n",
    "    da1 = np.dot(W2.T, dz2)\n",
    "    dz1 = np.multiply(da1, np.int64(a1 > 0))\n",
    "    dW1 = np.dot(dz1, X.T)\n",
    "    db1 = np.sum(dz1, axis=1, keepdims = True)\n",
    "    \n",
    "    gradients = {\"dz3\": dz3, \"dW3\": dW3, \"db3\": db3,\n",
    "                 \"da2\": da2, \"dz2\": dz2, \"dW2\": dW2, \"db2\": db2,\n",
    "                 \"da1\": da1, \"dz1\": dz1, \"dW1\": dW1, \"db1\": db1}\n",
    "    \n",
    "    return gradients\n",
    "def update_parameters(parameters, grads, learning_rate):  \n",
    "    L = len(parameters) // 2 # number of layers in the neural networks\n",
    "    # Update rule for each parameter\n",
    "    for k in range(L):\n",
    "        parameters[\"W\" + str(k+1)] = parameters[\"W\" + str(k+1)] - learning_rate * grads[\"dW\" + str(k+1)]\n",
    "        parameters[\"b\" + str(k+1)] = parameters[\"b\" + str(k+1)] - learning_rate * grads[\"db\" + str(k+1)]        \n",
    "    return parameters\n",
    "def model(X, Y, learning_rate = 0.01, num_iterations = 15000, print_cost = True, initialization = \"he\"):      \n",
    "    grads = {}\n",
    "    costs = [] # to keep track of the loss\n",
    "    m = X.shape[1] # number of examples\n",
    "    layers_dims = [X.shape[0], 10, 5, 1]    \n",
    "    # Initialize parameters dictionary.\n",
    "    if initialization == \"zeros\":\n",
    "        parameters = initialize_parameters_zeros(layers_dims)\n",
    "    elif initialization == \"random\":\n",
    "        parameters = initialize_parameters_random(layers_dims)\n",
    "    elif initialization == \"he\":\n",
    "        parameters = initialize_parameters_he(layers_dims)\n",
    "    # Loop (gradient descent)\n",
    "    for i in range(0, num_iterations):\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
    "        a3, cache = forward_propagation(X, parameters)        \n",
    "        # Loss\n",
    "        cost = compute_loss(a3, Y)\n",
    "        # Backward propagation.\n",
    "        grads = backward_propagation(X, Y, cache)        \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)        \n",
    "        # Print the loss every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
    "            costs.append(cost)            \n",
    "    # plot the loss\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per hundreds)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()    \n",
    "    return parameters\n",
    "def predict_dec(parameters, X):    \n",
    "    # Predict using forward propagation and a classification threshold of 0.5\n",
    "    a3, cache = forward_propagation(X, parameters)\n",
    "    predictions = (a3>0.5)\n",
    "    return predictions\n",
    "def predict(X, y, parameters):  \n",
    "    m = X.shape[1]\n",
    "    p = np.zeros((1,m), dtype = np.int)    \n",
    "    # Forward propagation\n",
    "    a3, caches = forward_propagation(X, parameters)    \n",
    "    # convert probas to 0/1 predictions\n",
    "    for i in range(0, a3.shape[1]):\n",
    "        if a3[0,i] > 0.5:\n",
    "            p[0,i] = 1\n",
    "        else:\n",
    "            p[0,i] = 0\n",
    "    # print results\n",
    "    print(\"Accuracy: \"  + str(np.mean((p[0,:] == y[0,:]))))    \n",
    "    return p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 零参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_zeros(layers_dims):   \n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # number of layers in the network   \n",
    "    for l in range(1, L):\n",
    "        parameters['W' + str(l)] = np.zeros((layers_dims[l],layers_dims[l-1]))\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W1': array([[0., 0., 0.],\n",
       "        [0., 0., 0.]]), 'b1': array([[0.],\n",
       "        [0.]]), 'W2': array([[0., 0.]]), 'b2': array([[0.]])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initialize_parameters_zeros([3,2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6931471805599453\n",
      "Cost after iteration 1000: 0.6931471805599453\n",
      "Cost after iteration 2000: 0.6931471805599453\n",
      "Cost after iteration 3000: 0.6931471805599453\n",
      "Cost after iteration 4000: 0.6931471805599453\n",
      "Cost after iteration 5000: 0.6931471805599453\n",
      "Cost after iteration 6000: 0.6931471805599453\n",
      "Cost after iteration 7000: 0.6931471805599453\n",
      "Cost after iteration 8000: 0.6931471805599453\n",
      "Cost after iteration 9000: 0.6931471805599453\n",
      "Cost after iteration 10000: 0.6931471805599455\n",
      "Cost after iteration 11000: 0.6931471805599453\n",
      "Cost after iteration 12000: 0.6931471805599453\n",
      "Cost after iteration 13000: 0.6931471805599453\n",
      "Cost after iteration 14000: 0.6931471805599453\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.5\n",
      "On the test set:\n",
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"zeros\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),\n",
       " array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_train,predictions_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, X, y):\n",
    "    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n",
    "    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n",
    "    h = 0.01\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    Z = model(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
    "    plt.ylabel('x2')\n",
    "    plt.xlabel('x1')\n",
    "    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with Zeros initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_random(layers_dims):    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)    \n",
    "    for l in range(1, L):\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1])*10\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W1': array([[ 17.88628473,   4.36509851,   0.96497468],\n",
       "        [-18.63492703,  -2.77388203,  -3.54758979]]), 'b1': array([[0.],\n",
       "        [0.]]), 'W2': array([[-0.82741481, -6.27000677]]), 'b2': array([[0.]])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initialize_parameters_random([3, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\pythonve\\test36\\lib\\site-packages\\ipykernel_launcher.py:27: RuntimeWarning: divide by zero encountered in log\n",
      "d:\\env\\pythonve\\test36\\lib\\site-packages\\ipykernel_launcher.py:27: RuntimeWarning: invalid value encountered in multiply\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: inf\n",
      "Cost after iteration 1000: 0.6239567039908781\n",
      "Cost after iteration 2000: 0.5978043872838292\n",
      "Cost after iteration 3000: 0.563595830364618\n",
      "Cost after iteration 4000: 0.5500816882570866\n",
      "Cost after iteration 5000: 0.5443417928662615\n",
      "Cost after iteration 6000: 0.5373553777823036\n",
      "Cost after iteration 7000: 0.4700141958024487\n",
      "Cost after iteration 8000: 0.3976617665785177\n",
      "Cost after iteration 9000: 0.39344405717719166\n",
      "Cost after iteration 10000: 0.39201765232720626\n",
      "Cost after iteration 11000: 0.38910685278803786\n",
      "Cost after iteration 12000: 0.38612995897697244\n",
      "Cost after iteration 13000: 0.3849735792031832\n",
      "Cost after iteration 14000: 0.38275100578285265\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.83\n",
      "On the test set:\n",
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"random\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n",
       "         1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "         1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "         0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "         1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "         1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,\n",
       "         1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "         1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
       "         0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
       "         1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
       "         1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
       "         1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,\n",
       "         0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "         1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0]]),\n",
       " array([[1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "         1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "         1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "         1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0]]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_train,predictions_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with large random initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## \"He\"参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_he(layers_dims):\n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layers_dims) - 1     \n",
    "    for l in range(1, L + 1):\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1])*np.sqrt(2./layers_dims[l-1])\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W1': array([[ 1.78862847,  0.43650985],\n",
       "        [ 0.09649747, -1.8634927 ],\n",
       "        [-0.2773882 , -0.35475898],\n",
       "        [-0.08274148, -0.62700068]]), 'b1': array([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]]), 'W2': array([[-0.03098412, -0.33744411, -0.92904268,  0.62552248]]), 'b2': array([[0.]])}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initialize_parameters_he([2, 4, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.8830537463419761\n",
      "Cost after iteration 1000: 0.6879825919728063\n",
      "Cost after iteration 2000: 0.6751286264523371\n",
      "Cost after iteration 3000: 0.6526117768893807\n",
      "Cost after iteration 4000: 0.6082958970572938\n",
      "Cost after iteration 5000: 0.5304944491717495\n",
      "Cost after iteration 6000: 0.4138645817071794\n",
      "Cost after iteration 7000: 0.3117803464844441\n",
      "Cost after iteration 8000: 0.23696215330322562\n",
      "Cost after iteration 9000: 0.18597287209206836\n",
      "Cost after iteration 10000: 0.15015556280371817\n",
      "Cost after iteration 11000: 0.12325079292273552\n",
      "Cost after iteration 12000: 0.09917746546525932\n",
      "Cost after iteration 13000: 0.08457055954024274\n",
      "Cost after iteration 14000: 0.07357895962677362\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.9933333333333333\n",
      "On the test set:\n",
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"he\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with He initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "1. 不同的初始化参数会带来不同的模型学习效果;\n",
    "2. 随机初始化参数的方法能够避免对称效应,使得同一隐含层的不同节点学习到不同的特征;\n",
    "3. 不能使用太大的初始化参数,\"He\"参数初始化方法在有relu激活单元的神经网络中,有很好的表现;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则化-练习\n",
    "深度神经网络在不采用避免手段的时候,很容易过度拟合训练集,特别是在训练集不足够大的时候,这种情况也叫做过度优化.下面我们来练习使用正则化的方法避免模型过度优化.\n",
    "\n",
    "**问题:**\n",
    "\n",
    "<img src=\"images/field_kiank.png\"  style=\"width:600px;height:350px;\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "def load_2D_dataset():\n",
    "    data = scipy.io.loadmat('datasets/data.mat')\n",
    "    train_X = data['X'].T\n",
    "    train_Y = data['y'].T\n",
    "    test_X = data['Xval'].T\n",
    "    test_Y = data['yval'].T\n",
    "\n",
    "    plt.scatter(train_X[0, :], train_X[1, :], c=train_Y.ravel(), s=40, cmap=plt.cm.Spectral);\n",
    "    \n",
    "    return train_X, train_Y, test_X, test_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_X, train_Y, test_X, test_Y = load_2D_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(layer_dims):    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layer_dims) # number of layers in the network\n",
    "\n",
    "    for l in range(1, L):\n",
    "        parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) / np.sqrt(layer_dims[l-1])\n",
    "        parameters['b' + str(l)] = np.zeros((layer_dims[l], 1)) \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X, Y, learning_rate = 0.3, num_iterations = 30000, print_cost = True, lambd = 0, keep_prob = 1):\n",
    "    \"\"\"\n",
    "    Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (input size, number of examples)\n",
    "    Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (output size, number of examples)\n",
    "    learning_rate -- learning rate of the optimization\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    print_cost -- If True, print the cost every 10000 iterations\n",
    "    lambd -- regularization hyperparameter, scalar\n",
    "    keep_prob - probability of keeping a neuron active during drop-out, scalar.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learned by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "        \n",
    "    grads = {}\n",
    "    costs = []                            # to keep track of the cost\n",
    "    m = X.shape[1]                        # number of examples\n",
    "    layers_dims = [X.shape[0], 20, 3, 1]\n",
    "    \n",
    "    # Initialize parameters dictionary.\n",
    "    parameters = initialize_parameters(layers_dims)\n",
    "\n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
    "        if keep_prob == 1:\n",
    "            a3, cache = forward_propagation(X, parameters)\n",
    "        elif keep_prob < 1:\n",
    "            a3, cache = forward_propagation_with_dropout(X, parameters, keep_prob)\n",
    "        \n",
    "        # Cost function\n",
    "        if lambd == 0:\n",
    "            cost = compute_loss(a3, Y)\n",
    "        else:\n",
    "            cost = compute_cost_with_regularization(a3, Y, parameters, lambd)\n",
    "            \n",
    "        # Backward propagation.\n",
    "        assert(lambd==0 or keep_prob==1)    # it is possible to use both L2 regularization and dropout, \n",
    "                                            # but this assignment will only explore one at a time\n",
    "        if lambd == 0 and keep_prob == 1:\n",
    "            grads = backward_propagation(X, Y, cache)\n",
    "        elif lambd != 0:\n",
    "            grads = backward_propagation_with_regularization(X, Y, cache, lambd)\n",
    "        elif keep_prob < 1:\n",
    "            grads = backward_propagation_with_dropout(X, Y, cache, keep_prob)\n",
    "        \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        \n",
    "        # Print the loss every 10000 iterations\n",
    "        if print_cost and i % 10000 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            costs.append(cost)\n",
    "    \n",
    "    # plot the cost\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (x1,000)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6557412523481002\n",
      "Cost after iteration 10000: 0.16329987525724216\n",
      "Cost after iteration 20000: 0.1385164242327309\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the training set:\n",
      "Accuracy: 0.9478672985781991\n",
      "On the test set:\n",
      "Accuracy: 0.915\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y)\n",
    "print (\"On the training set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model without regularization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $L2$正则化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$J = -\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small  y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} \\tag{1}$$\n",
    "To:\n",
    "$$J_{regularized} = \\small \\underbrace{-\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} }_\\text{cross-entropy cost} + \\underbrace{\\frac{1}{m} \\frac{\\lambda}{2} \\sum\\limits_l\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2} }_\\text{L2 regularization cost} \\tag{2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost_with_regularization(A3, Y, parameters, lambd):\n",
    "    m = Y.shape[1]\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    W3 = parameters[\"W3\"]    \n",
    "    cross_entropy_cost = compute_loss(A3, Y) \n",
    "    L2_regularization_cost = (1./m*lambd/2)*(np.sum(np.square(W1)) + np.sum(np.square(W2)) + np.sum(np.square(W3)))    \n",
    "    cost = cross_entropy_cost + L2_regularization_cost    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ($\\frac{d}{dW} ( \\frac{1}{2}\\frac{\\lambda}{m}  W^2) = \\frac{\\lambda}{m} W$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation_with_regularization(X, Y, cache, lambd):\n",
    "    m = X.shape[1]\n",
    "    (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
    "\n",
    "    dZ3 = A3 - Y\n",
    "    \n",
    "    dW3 = 1./m * np.dot(dZ3, A2.T) + lambd/m * W3\n",
    "    db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n",
    "\n",
    "    dA2 = np.dot(W3.T, dZ3)\n",
    "    dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
    "    dW2 = 1./m * np.dot(dZ2, A1.T) + lambd/m * W2\n",
    "    db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n",
    "\n",
    "    dA1 = np.dot(W2.T, dZ2)\n",
    "    dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
    "    dW1 = 1./m * np.dot(dZ1, X.T) + lambd/m * W1\n",
    "    db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n",
    "\n",
    "    gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n",
    "                 \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
    "                 \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
    "\n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6974484493131264\n",
      "Cost after iteration 10000: 0.26849188732822393\n",
      "Cost after iteration 20000: 0.2680916337127301\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.9383886255924171\n",
      "On the test set:\n",
      "Accuracy: 0.93\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, lambd = 0.7)\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with L2-regularization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**总结:**\n",
    "1. $L2$正则化的$\\lambda$超参数,能够调整模型在验证集(测试集)的表现;\n",
    "2. $L2$正则化可以使得分类边界较之平滑,如果$\\lambda$太大的话,会导致分类边界过平滑,从而导致模型偏差过大.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $Dropout$\n",
    "$Dropout$正则化是深度学习中广泛使用的正则化方法.正如前面介绍,它会在每次迭代中随机丢弃一些节点.实际上,每次迭代随机丢弃节点,相当于调整了我们模型的结构,可以看做更换了模型.$Dropout$的思想是,每次迭代随机使用模型的某个子集进行训练.这样我们的神经网络不会依赖某一个固定神经元的表现了.从而使得模型获得了一定的泛化能力."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation_with_dropout(X, parameters, keep_prob = 0.5):\n",
    "    np.random.seed(1)\n",
    "    \n",
    "    # retrieve parameters\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    \n",
    "    # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    Z1 = np.dot(W1, X) + b1\n",
    "    A1 = relu(Z1)\n",
    "    D1 = np.random.rand(A1.shape[0],A1.shape[1])# Step 1: initialize matrix D1 = np.random.rand(..., ...)\n",
    "    D1 = D1 < keep_prob                         # Step 2: convert entries of D1 to 0 or 1 (using keep_prob as the threshold)\n",
    "    A1 = A1 * D1                                # Step 3: shut down some neurons of A1\n",
    "    A1 = A1 / keep_prob                         # Step 4: scale the value of neurons that haven't been shut down\n",
    "    \n",
    "    Z2 = np.dot(W2, A1) + b2\n",
    "    A2 = relu(Z2)\n",
    "    D2 = np.random.rand(A2.shape[0],A2.shape[1])# Step 1: initialize matrix D2 = np.random.rand(..., ...)\n",
    "    D2 = D2 < keep_prob                         # Step 2: convert entries of D2 to 0 or 1 (using keep_prob as the threshold)\n",
    "    A2 = A2 * D2                                # Step 3: shut down some neurons of A2\n",
    "    A2 = A2 / keep_prob                         # Step 4: scale the value of neurons that haven't been shut down\n",
    "    Z3 = np.dot(W3, A2) + b3\n",
    "    A3 = sigmoid(Z3)\n",
    "    cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)\n",
    "    return A3, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation_with_dropout(X, Y, cache, keep_prob):    \n",
    "    m = X.shape[1]\n",
    "    (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
    "    \n",
    "    dZ3 = A3 - Y\n",
    "    dW3 = 1./m * np.dot(dZ3, A2.T)\n",
    "    db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n",
    "    \n",
    "    dA2 = np.dot(W3.T, dZ3)\n",
    "    dA2 = dA2 * D2              # Step 1: Apply mask D2 to shut down the same neurons as during the forward propagation\n",
    "    dA2 = dA2 / keep_prob       # Step 2: Scale the value of neurons that haven't been shut down\n",
    "    dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
    "    dW2 = 1./m * np.dot(dZ2, A1.T)\n",
    "    db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n",
    "\n",
    "    dA1 = np.dot(W2.T, dZ2)\n",
    "    dA1 = dA1 * D1              # Step 1: Apply mask D1 to shut down the same neurons as during the forward propagation\n",
    "    dA1 = dA1 / keep_prob       # Step 2: Scale the value of neurons that haven't been shut down\n",
    "    dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
    "    dW1 = 1./m * np.dot(dZ1, X.T)\n",
    "    db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n",
    "    \n",
    "    gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n",
    "                 \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
    "                 \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
    "    \n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6543912405149825\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\pythonve\\test36\\lib\\site-packages\\ipykernel_launcher.py:27: RuntimeWarning: divide by zero encountered in log\n",
      "d:\\env\\pythonve\\test36\\lib\\site-packages\\ipykernel_launcher.py:27: RuntimeWarning: invalid value encountered in multiply\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 10000: 0.0610169865749056\n",
      "Cost after iteration 20000: 0.060582435798513114\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.9289099526066351\n",
      "On the test set:\n",
      "Accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, keep_prob = 0.86, learning_rate = 0.3)\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with dropout\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**总结:**\n",
    "1. $Dropout$正则化只在训练集上使用,不在验证集或者测试集上使用;\n",
    "2. <table> \n",
    "    <tr>\n",
    "        <td>\n",
    "        **model**\n",
    "        </td>\n",
    "        <td>\n",
    "        **train accuracy**\n",
    "        </td>\n",
    "        <td>\n",
    "        **test accuracy**\n",
    "        </td>\n",
    "    </tr>\n",
    "        <td>\n",
    "        3-layer NN without regularization\n",
    "        </td>\n",
    "        <td>\n",
    "        95%\n",
    "        </td>\n",
    "        <td>\n",
    "        91.5%\n",
    "        </td>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with L2-regularization\n",
    "        </td>\n",
    "        <td>\n",
    "        94%\n",
    "        </td>\n",
    "        <td>\n",
    "        93%\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with dropout\n",
    "        </td>\n",
    "        <td>\n",
    "        93%\n",
    "        </td>\n",
    "        <td>\n",
    "        95%\n",
    "        </td>\n",
    "    </tr>\n",
    "</table> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "557px",
    "width": "334px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
